Comparative Performance of Advanced NLP Models and LLMs in Multilingual Geo-Entity Detection

Kalin Kopanov

REVIEW 3 major objections 6 minor 11 references

Comparative Performance of Advanced NLP Models and LLMs in Multilingual Geo-Entity Detection

T0 review · 3 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper reports a six-model benchmark in which XLM-RoBERTa and GPT-4 achieve the best balanced geo-entity F1 across English, Russian, and Arabic, while SpaCy and mLUKE detect no Arabic locations at all.

desk verdict A straightforward six-model benchmark on Telegram data whose headline F1 numbers are not actually computable from the method as written, because recall is never grounded in a full gold-standard pass. read the letter →

arxiv 2412.20414 v1 pith:AQQTFHKA submitted 2024-12-29 cs.CL cs.AIcs.IR

Kalin Kopanov This is my paper · ORCID

classification cs.CLcs.AIcs.IR

keywords NamedEntityRecognitiongeospatialdetectionmultilingualNLPlargelanguagemodelsTelegramdataXLM-RoBERTaGPT-4GeoLM

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper reports a benchmark of six named-entity recognition systems for detecting geographical references in multilingual Telegram posts: SpaCy, XLM-RoBERTa, mLUKE, GeoLM, GPT-3.5, and GPT-4. The central claim is that on posts in English, Russian, and Arabic about ongoing conflicts, XLM-RoBERTa and GPT-4 achieve the best balanced F1 scores, while SpaCy and mLUKE detect no Arabic locations at all and GeoLM is nearly ineffective outside English. The stakes are practical: security monitoring, crisis response, and situational awareness depend on reliably extracting location names from social media in many languages, and model choice should follow from measured cross-lingual coverage.

What carries the argument

The evaluation pipeline is the mechanism: a custom Python script normalizes Telegram raw text, runs each model sequentially to extract location mentions, then submits every detected entity to manual verification before computing precision, recall, and F1 per language. The six models are SpaCy with language-specific and multi-language pipelines, XLM-RoBERTa fine-tuned on CoNLL-2003, mLUKE whose token spans come from SpaCy, GeoLM's toponym-recognition model, and GPT-3.5 and GPT-4 accessed through their API with a custom location-only prompt. The pipeline's design means the Arabic 0.00 scores for SpaCy and mLUKE are a direct consequence of SpaCy's generic multi-language model being the only Arabic path.

What would settle it

Re-verify a random sample of the same Telegram posts using multiple annotators and a written guideline for multi-word and pejorative location names, then recompute F1; if the new labels disagree substantially with the paper's manual verification, the reported rankings and especially the Arabic 0.00 scores would change.

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Extended reading notes

Core claim

Across the curated Telegram corpus (1,054 English posts, 2,406 Russian, 1,065 Arabic), XLM-RoBERTa and GPT-4 tie or lead in every language: F1 of 0.90 and 0.90 in English, 0.91 and 0.90 in Russian, and 0.84 and 0.74 in Arabic. SpaCy and mLUKE both score precision, recall, and F1 of 0.00 on Arabic; GeoLM, an English-centric geospatial language model, drops from 0.69 F1 in English to 0.15 in Russian and 0.00 in Arabic; GPT-3.5 is mediocre everywhere, with its best F1 0.58 in English. The paper attributes SpaCy's Arabic failure to its generic multi-language pipeline, mLUKE's failure to its reliance on SpaCy for span detection, and GeoLM's to its English-only training, and it flags the 512-token limit of mLUKE and GeoLM plus hard cases like multi-word and pejorative location terms as additional causes of errors.

Load-bearing premise

The load-bearing premise is that the manual verification of every detected entity is accurate and consistent, but the paper gives no annotation protocol, no number of annotators, and no inter-annotator agreement, so noisy gold labels would shift every reported score.

Editorial extensions

If this is right

For short multilingual posts, XLM-RoBERTa and GPT-4 are the only evaluated models with usable geo-entity F1 across English, Russian, and Arabic.
Any system depending on SpaCy's generic multi-language pipeline will also fail on Arabic, as mLUKE did.
GeoLM should be treated as an English-only model until it receives cross-lingual adaptation.
The 512-token truncation of mLUKE and GeoLM is a practical constraint even for brief Telegram messages.
GPT-3.5's inconsistent location detection makes it a weak default compared with GPT-4.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The Arabic zero scores are likely an artifact of model selection rather than an inherent limitation: a dedicated Arabic SpaCy model or another Arabic NER would be a direct test of whether SpaCy's ecosystem can handle Arabic.
The reported F1 differences between XLM-RoBERTa and GPT-4 are within a few points; without confidence intervals or significance testing, claims of superiority for either model are not established.
A formal annotation guideline for multi-word and pejorative geographic names would likely change recall values substantially, since the paper itself identifies these as the main error sources.
The same comparison could be extended to a public multilingual NER benchmark to test whether the observed cross-lingual gaps generalize beyond this Telegram corpus.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Read this one carefully before trusting any number in Table 1. It is a small multilingual benchmark comparing SpaCy, XLM-RoBERTa, mLUKE, GeoLM, GPT-3.5 and GPT-4 on geo-entity detection in English, Russian and Arabic Telegram posts. The empirical scores are new, and the qualitative findings match what I would expect: SpaCy and mLUKE collapse on Arabic, GeoLM is English-centric, GPT-3.5 is mediocre, and XLM-RoBERTa and GPT-4 lead on F1. The model selection is sensible, the data sources are identified, and the authors are honest about practical constraints like the 512-token limit and hard cases such as multi-word and offensive toponyms.

The soft spot is not minor. The paper's evaluation protocol only manually verifies entities the models identified. That yields true positives and false positives, but never false negatives. Without a complete annotation of every true location mention in the corpus, you cannot compute recall, and every F1 score in Table 1 is built on recall. XLM-RoBERTa's English recall of 0.98 would mean it found nearly all true mentions, which cannot be established by inspecting only its own output. Either a full gold-standard pass was performed and omitted from the write-up, or the reported recall and F1 values are not well-defined. The paper also promises an accuracy metric that never appears, and provides no dataset, code, annotation guidelines, inter-annotator agreement, or error bars. The exact numbers are therefore unverifiable from the text as-is.

That said, the central ranking is plausible, and the paper is not circular or incoherent in its reasoning. It is simply missing the load-bearing part of its own evaluation. The fix is clear: release the annotated data and code, describe how false negatives were established, and report variance. With that, this could be a useful practical reference for practitioners choosing off-the-shelf models for multilingual monitoring. As written, though, I would not cite the scores.

Send it to serious peer review. The question is legitimate and the paper is worth engaging; a good referee can force the authors to close the evaluation gap. But prepare for major revision.

Referee Report

3 major / 6 minor

Summary. The paper compares six NLP models—SpaCy, XLM-RoBERTa, mLUKE, GeoLM, GPT-3.5, and GPT-4—for geo-entity detection in Telegram posts in English, Russian, and Arabic. It reports precision, recall, and F1 scores in Table 1 and concludes that XLM-RoBERTa and GPT-4 achieve the best balanced performance. The authors describe a pipeline that applies each model sequentially and manually verifies the geographic entities identified by the models. The paper does not release its dataset, code, annotation guidelines, or inter-annotator agreement measures.

Significance. If the numerical results were trustworthy, the paper would provide a practically useful comparison for security and crisis-monitoring applications. The selection of models is reasonable, and the focus on multilingual Telegram data is relevant. However, the evaluation protocol as described cannot support the reported recall and F1 values, and the promised accuracy metric is absent. The paper also lacks the reproducibility artifacts (data, code, annotation protocol) that would allow independent verification. These gaps are not cosmetic; they undermine the central comparative claim.

major comments (3)

[§4.2, Table 1] The stated manual verification protocol cannot yield the recall or F1 values in Table 1. Section 4.2 says that 'each geographical entity identified by the models was subjected to manual verification,' which labels model predictions as true or false positives but provides no procedure for identifying false negatives—entities present in the text that no model detected. Without a full gold-standard annotation of the corpus, the denominator of recall (TP + FN) is undefined, so every recall and F1 entry in Table 1 is unsupported. For instance, XLM-RoBERTa's English recall of 0.98 would require knowing that nearly all true location mentions in the corpus appear among its predictions, which cannot be established by checking only its own output. The paper must either supply the complete gold-standard annotation procedure (including annotator count, instructions, and inter-annotator agreement) or restrict its claims to precision, which is the only metric computable from the described protocol.
[Abstract and §4.3] The abstract states that the paper examines performance 'through metrics such as accuracy, precision, recall, and F1 scores,' and Section 4.3 is titled 'Accuracy Assessment,' yet no accuracy values are reported anywhere in the paper, including in Table 1. This is an internal inconsistency. The authors should either report the accuracy metric they promised (with a clear definition, e.g., token-level or entity-level accuracy) or explicitly remove 'accuracy' from the abstract and Section 4.3.
[§3.6, §4.2, References [11]–[14]] The experimental results are not reproducible because the dataset is not described at the level needed to reconstruct it. The paper gives only channel URLs and counts of posts per channel; it does not provide post identifiers, timestamps, or a data availability statement. Moreover, no annotation guidelines, annotator qualifications, or inter-annotator agreement are reported for the manual verification, and the evaluation script is not released. For a comparative benchmark paper, these details are necessary for readers to trust or replicate the reported scores. The authors should make the annotated data and code available, or at minimum provide a detailed and deterministic description of the data collection and annotation process.

minor comments (6)

[§4.4] Section 4.4 states that 'the inclusion of tables or graphs summarizing the evaluation results is planned,' although Table 1 is already included in Section 5. This suggests leftover text from an earlier draft and should be reconciled.
[Table 1] The table is difficult to parse as typeset: the model names and metric values run together, and the footnote 'a' appears detached. Please reformat it as a proper grid with clear column separators.
[§3.1] The description of the SpaCy multi-language model says it is 'presumably' for Arabic; the paper should specify exactly which model is used for Arabic and why no dedicated Arabic SpaCy model is employed.
[§3.5] The prose refers to 'GPT-3.5' and 'GPT-4' while Section 3.5 gives specific API names 'gpt-3.5-turbo-0125' and 'gpt-4-0125-preview.' Please use the exact model identifiers consistently throughout the paper, including in Table 1.
[Global] The paper uses 'F-1 score,' 'F1 Score,' and 'F1' inconsistently. Please standardize the terminology.
[References [11]–[14]] The Telegram channel references would be more useful if they included the date of data collection or the specific date range of the retrieved posts, rather than only a general retrieval date.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the paper reports direct evaluation metrics of pretrained models against manual labels, with no fitted parameters, no prediction-from-fit chain, and no load-bearing self-citation.

full rationale

The paper's claimed result is a comparative performance evaluation of six existing NLP models and LLMs on a Telegram corpus in English, Russian, and Arabic. The reported precision, recall, and F1 values in Table 1 are computed directly from model outputs that are manually verified as true or false positives. There is no fitted model, no parameter estimated from one subset and then used to predict another, and no derivation that relies on the paper's own prior work. The dependency of mLUKE on SpaCy for initial span detection is a pipeline design choice, not circular reasoning, because the evaluated output is still observed from mLUKE's own entity-centric processing. The abstract's promise of an 'accuracy' metric and the absence of a full false-negative enumeration in the stated protocol are reproducibility and validity concerns, but they do not make the reported numbers equivalent to the inputs by construction. Since no step in the evaluation chain reduces to its own input, the circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The central numbers rest entirely on the manual ground truth and the sampled channels; no parameters are fitted, but nonstandard ground-truth assumptions are load-bearing.

assumptions (3)

domain assumption Manual verification of each detected entity provides a correct and consistent ground truth.
Section 4.2 states all identified entities were manually verified, but no annotation protocol, coder agreement, or error analysis is given. All precision, recall, and F1 values depend on this assumption.
domain assumption The selected Telegram channels and time window form an adequate sample for estimating multilingual geo-entity detection performance.
Section 3.6 describes four channels from January and February 2024; the paper acknowledges they are a real-world sample and not meant to measure bias, yet draws general conclusions about model capabilities.
standard math Standard precision, recall, and F1 definitions apply with a single reference labeling per entity.
Section 4.2 defines the metrics in the usual way; partial credit and multi-word overlaps are not specified.

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Cite this review

Pith. "Pith review of Comparative Performance of Advanced NLP Models and LLMs in Multilingual Geo-Entity Detection." pith.science (2026). https://pith.science/paper/AQQTFHKA

@misc{pith2026241220414,
  author       = {Pith},
  title        = {Pith review of: Comparative Performance of Advanced NLP Models and LLMs in Multilingual Geo-Entity Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AQQTFHKA}},
  note         = {Machine review of arXiv:2412.20414}
}

read the original abstract

The integration of advanced Natural Language Processing (NLP) methodologies and Large Language Models (LLMs) has significantly enhanced the extraction and analysis of geospatial data from multilingual texts, impacting sectors such as national and international security. This paper presents a comprehensive evaluation of leading NLP models -- SpaCy, XLM-RoBERTa, mLUKE, GeoLM -- and LLMs, specifically OpenAI's GPT 3.5 and GPT 4, within the context of multilingual geo-entity detection. Utilizing datasets from Telegram channels in English, Russian, and Arabic, we examine the performance of these models through metrics such as accuracy, precision, recall, and F1 scores, to assess their effectiveness in accurately identifying geospatial references. The analysis exposes each model's distinct advantages and challenges, underscoring the complexities involved in achieving precise geo-entity identification across varied linguistic landscapes. The conclusions drawn from this experiment aim to direct the enhancement and creation of more advanced and inclusive NLP tools, thus advancing the field of geospatial analysis and its application to global security.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

11 extracted references · 7 canonical work pages

[1]

Precision:Thismetricmeasurestheaccuracyofthemodelinidentifyingpositiveinstances.Itquantifiestheratioofcorrectlyidentifiedgeographicalentities(TruePositives)tothetotalnumberofentitiesthemodelidentified(sumofTruePositivesandFalsePositives).Highprecisionindicatesthatamodeliseffectiveinminimizingfalsepositives.Theformulaforprecisionis:Precision=(TruePositives...

work page
[2]

Recall:Thismetricevaluatesthemodel'sabilitytoidentifyallrelevantinstanceswithinthedataset.It iscalculatedastheratioofTruePositivestothetotalactualpositives(sumofTruePositivesandFalseNegatives).Highrecallimpliesthatthemodeleffectivelyminimizesfalsenegatives,capturinga higherproportionof actualgeographicalentities.Theformulaforrecallis: Recall=(TruePositive...

work page
[3]

BeirutRaficHaririInternationalAirport

F1Score:Servingastheharmonicmeanof PrecisionandRecall,theF1Scoreprovidesa singlemetrictoassessthebalancebetweenPrecisionandRecall.It isparticularlyusefulwhenthecostoffalsepositivesandfalsenegativesvariesorwhenoneseeksa balancebetweenidentifyingasmanypositivesaspossiblewhileminimizingincorrectidentifications.TheF1Scoreisdefinedas: F1Score=2*(Precision*Reca...

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Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020). UnsupervisedCross-lingual Representation Learning at Scale. In D. Jurafsky, J. Chai, N. Schluter, & J. Tetreault (Eds.), Proceedings of the 58th Annual Meeting of theAssociationfor Computational Linguistics(pp. 8440–84...

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [1]

Precision:Thismetricmeasurestheaccuracyofthemodelinidentifyingpositiveinstances.Itquantifiestheratioofcorrectlyidentifiedgeographicalentities(TruePositives)tothetotalnumberofentitiesthemodelidentified(sumofTruePositivesandFalsePositives).Highprecisionindicatesthatamodeliseffectiveinminimizingfalsepositives.Theformulaforprecisionis:Precision=(TruePositives...

work page

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Recall:Thismetricevaluatesthemodel'sabilitytoidentifyallrelevantinstanceswithinthedataset.It iscalculatedastheratioofTruePositivestothetotalactualpositives(sumofTruePositivesandFalseNegatives).Highrecallimpliesthatthemodeleffectivelyminimizesfalsenegatives,capturinga higherproportionof actualgeographicalentities.Theformulaforrecallis: Recall=(TruePositive...

work page

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BeirutRaficHaririInternationalAirport

F1Score:Servingastheharmonicmeanof PrecisionandRecall,theF1Scoreprovidesa singlemetrictoassessthebalancebetweenPrecisionandRecall.It isparticularlyusefulwhenthecostoffalsepositivesandfalsenegativesvariesorwhenoneseeksa balancebetweenidentifyingasmanypositivesaspossiblewhileminimizingincorrectidentifications.TheF1Scoreisdefinedas: F1Score=2*(Precision*Reca...

2020

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Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020). UnsupervisedCross-lingual Representation Learning at Scale. In D. Jurafsky, J. Chai, N. Schluter, & J. Tetreault (Eds.), Proceedings of the 58th Annual Meeting of theAssociationfor Computational Linguistics(pp. 8440–84...

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Yamada, I., Asai, A., Shindo, H., Takeda, H., & Matsumoto, Y. (2020). LUKE: Deep Contextualized Entity RepresentationswithEntity-awareSelf-attention. InB.Webber, T. Cohn, Y. He, & Y. Liu (Eds.), Proceedings of the 2020ConferenceonEmpirical MethodsinNatural LanguageProcessing(EMNLP) (pp. 6442–6454).Associationfor Computational Linguistics. https://doi.org/...

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Li, Z., Zhou, W., Chiang, Y.-Y., & Chen, M. (2023). GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding. arXiv.arXiv:2310.14478v1[cs.CL]

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(2024, March21)

ExplosionAI. (2024, March21). TrainedModels&Pipelines. SpaCy. RetrievedMarch21, 2024, fromhttps://spacy.io/models/

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